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Finite Rank Series Modeling for Discrimination of Non-stationary Signals

  • Lina Maria Sepulveda-Cano
  • Carlos Daniel Acosta-Medina
  • Germán Castellanos-Dominguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

The analysis of time-variant biosignals for classification tasks, usually requires a modeling that may handel their different dynamics and non–stationary components. Although determination of proper stationary data length and the model parameters remains as an open issue. In this work, time–variant signal decomposition through Finite Rank Series Modeling is carried out, aiming to find the model parameters. Three schemes are tested for OSA detection based on HRV recordings: SSA and DLM as linear decompositions and EDS as non–linear decomposition. Results show that EDS decomposition presents the best performance, followed by SSA. As a conclusion, it can be inferred that adding complexity at the linear model the trend is approximate to a simple non–linear model.

Keywords

Obstructive Sleep Apnea Empirical Mode Decomposition Obstructive Sleep Apnoea Singular Spectral Analysis Obstructive Sleep Apnoea Syndrome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lina Maria Sepulveda-Cano
    • 1
  • Carlos Daniel Acosta-Medina
    • 1
  • Germán Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia

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